# Overview

We present for pre-trained models to illustrate the use of the *mlip* library. Below are the references for each of these models (MACE, NequIP, ViSNet, eSEN). 
# References
## MACE

Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, and Gábor Csányi.
Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,
2023. 

URL: https://arxiv.org/abs/2206.07697

## NequIP

Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, 
Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky. 
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. 
Nature Communications, 13(1), May 2022. ISSN: 2041-1723. 

URL: https://dx.doi.org/10.1038/s41467-022-29939-5.

## ViSNet

Yusong Wang, Tong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao, and Tie-Yan Liu.
Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.
Nature Communications, 15(1), January 2024. ISSN: 2041-1723.

URL: https://dx.doi.org/10.1038/s41467-023-43720-2.

## eSEN

Xiang Fu, Brandon M. Wood, Luis Barroso-Luque, Daniel S. Levine, Meng Gao, Misko
Dzamba, and C. Lawrence Zitnick. Learning smooth and expressive interatomic potentials for
physical property prediction, 2025. 

URL https://arxiv.org/abs/2502.12147.

# Relevant documentation

For a complete usage instructions, model configs and more information, please refer to our [documentation](https://instadeepai.github.io/mlip). 
We also present all model hyperparameters and training strategy in the associate [white paper](link.TBD).

These models were trained on the [SPICE2_curated dataset](https://huggingface.co/datasets/InstaDeepAI/SPICE2_curated_v2).
For more information about dataset configuration please refer to our [documentation](https://instadeepai.github.io/mlip/api_reference/data/dataset_configs.html#mlip.data.configs.GraphDatasetBuilderConfig)

# License summary

1. The Licensed Models are **only** available under this License for Non-Commercial Purposes.
2. You are permitted to reproduce, publish, share and adapt the Output generated by the Licensed Model only for Non-Commercial Purposes and in accordance with this License.
3. You may **not** use the Licensed Models or any of its Outputs in connection with:
    1. any Commercial Purposes, unless agreed by Us under a separate licence;
    2. to train, improve or otherwise influence the functionality or performance of any other third-party derivative model that is commercial or intended for a Commercial Purpose and is similar to the Licensed Models;
    3. to create models distilled or derived from the Outputs of the Licensed Models, unless such models are for Non-Commercial Purposes and open-sourced under the same license as the Licensed Models; or
    4. in violation of any applicable laws and regulations.